A practical diagram of the MFVC framework is displayed in Fig. REF , and this structure is mainly designed for low-delay scenarios (P-frame compression) without considering the bidirectional scenarios (B-frame).
The basic compression network is a unified auto-encoder model (similar to [1]}) for transforming all input frames \(x\)  into latents \({\hat{y}}\)  and restoring the reconstructed frames \({\hat{x}}\)  from the latents.
Latents can be losslessly compressed by arithmetic encoding (AE) [2]} and transmitted into a string of bits, using a probability distribution \(p_{\hat{y}}(\hat{y})\) .
When processing the first frame \(x_0\)  of each GOP, the probability distribution \(p_{\hat{y}_0}(\hat{y}_0)\)  is generated by a spatial entropy model same as that in image compression methods.
The probability distributions of other frames are all estimated by the spatiotemporal entropy model. In this work, the spatiotemporal entropy model and unified auto-encoder model are trained separately.
